Structural polymorphism of the HIV-1 leader region explored by computational methods

Experimental studies revealed that the elements of the human immunodeficiency virus type 1 (HIV-1) 5′-untranslated leader region (5′-UTR) can fold in vitro into two alternative conformations, branched (BMH) and ‘linearized’ (LDI) and switch between them to achieve different functionality. In this study we computationally explored in detail, with our massively parallel genetic algorithm (MPGAfold), the propensity of 13 HIV-1 5′-UTRs to fold into the BMH and the LDI conformation types. Besides the BMH conformations these results predict the existence of two functionally equivalent types of LDI conformations. One is similar to what has been shown in vitro to exist in HIV-1 LAI, the other is a novel conformation exemplified by HIV-1 MAL long-distance interactions. These novel MPGAfold results are further corroborated by a consensus probability matrix algorithm applied to a set of 155 HIV-1 sequences. We also have determined in detail the impact of various strain mutations, domain sizes and folds of elongating sequences simulating folding during transcription on HIV-1 RNA secondary structure folding dynamics

CorreLogo: an online server for 3D sequence logos of RNA and DNA alignments

We present an online server that generates a 3D representation of properties of user-submitted RNA or DNA alignments. The visualized properties are information of single alignment columns, mutual information of two alignment positions as well as the position-specific fraction of gaps. The nucleotide composition of both single columns and column pairs is visualized with the help of color-coded 3D bars labeled with letters. The server generates both VRML and JVX output that can be viewed with a VRML viewer or the JavaView applet, respectively. We show that combining these different features of an alignment into one 3D representation is helpful in identifying correlations between bases and potential RNA and DNA base pairs. Significant known correlations between the tRNA 3′ anticodon cardinal nucleotide and the extended anticodon were observed, as were correlations within the amino acid acceptor stem and between the cardinal nucleotide and the acceptor stem. The online server can be accessed using the URL

Computational and Experimental Characterization of RNA Cubic Nanoscaffolds

The fast-developing field of RNA nanotechnology requires the adoption and development of novel and faster computational approaches to modeling and characterization of RNA-based nano-objects. We report the first application of Elastic Network Modeling (ENM), a structure-based dynamics model, to RNA nanotechnology. With the use of an Anisotropic Network Model (ANM), a type of ENM, we characterize the dynamic behavior of non-compact, multi-stranded RNA-based nanocubes that can be used as nano-scale scaffolds carrying different functionalities. Modeling the nanocubes with our tool NanoTiler and exploring the dynamic characteristics of the models with ANM suggested relatively minor but important structural modifications that enhanced the assembly properties and thermodynamic stabilities. In silico and in vitro, we compared nanocubes having different numbers of base pairs per side, showing with both methods that the 10 bp-long helix design leads to more efficient assembly, as predicted computationally. We also explored the impact of different numbers of single-stranded nucleotide stretches at each of the cube corners and showed that cube flexibility simulations help explain the differences in the experimental assembly yields, as well as the measured nanomolecule sizes and melting temperatures. This original work paves the way for detailed computational analysis of the dynamic behavior of artificially designed multi-stranded RNA nanoparticles

IFNL3 (IL28B) favorable genotype escapes hepatitis C virus-induced microRNAs and mRNA decay

The IFNL3 (IL28B) gene has received immense attention in the hepatitis C virus (HCV) field as multiple independent genome-wide association studies identified a strong association between polymorphisms near the IFNL3 gene and HCV clearance. However, the mechanism underlying this association has remained elusive. In this study, we report the identification of a functional polymorphism (rs4803217) located in the 3′ untranslated region (3′ UTR) of the IFNL3 mRNA that dictates transcript stability. This polymorphism influences AU-rich element-mediated decay as well as the binding of HCV-induced microRNAs during infection. Together, these pathways mediate robust repression of the unfavorable IFNL3 genotype. These data reveal a novel mechanism by which HCV attenuates the antiviral response and uncover new potential therapeutic targets for HCV treatment

Role of 3′UTRs in the Translation of mRNAs Regulated by Oncogenic eIF4E—A Computational Inference

Eukaryotic cap-dependent mRNA translation is mediated by the initiation factor eIF4E, which binds mRNAs and stimulates efficient translation initiation. eIF4E is often overexpressed in human cancers. To elucidate the molecular signature of eIF4E target mRNAs, we analyzed sequence and structural properties of two independently derived polyribosome recruited mRNA datasets. These datasets originate from studies of mRNAs that are actively being translated in response to cells over-expressing eIF4E or cells with an activated oncogenic AKT: eIF4E signaling pathway, respectively. Comparison of eIF4E target mRNAs to mRNAs insensitive to eIF4E-regulation has revealed surprising features in mRNA secondary structure, length and microRNA-binding properties. Fold-changes (the relative change in recruitment of an mRNA to actively translating polyribosomal complexes in response to eIF4E overexpression or AKT upregulation) are positively correlated with mRNA G+C content and negatively correlated with total and 3′UTR length of the mRNAs. A machine learning approach for predicting the fold change was created. Interesting tendencies of secondary structure stability are found near the start codon and at the beginning of the 3′UTR region. Highly upregulated mRNAs show negative selection (site avoidance) for binding sites of several microRNAs. These results are consistent with the emerging model of regulation of mRNA translation through a dynamic balance between translation initiation at the 5′UTR and microRNA binding at the 3′UTR

RNA secondary structure prediction from sequence alignments using a network of k-nearest neighbor classifiers

We present a machine learning method (a hierarchical network of k-nearest neighbor classifiers) that uses an RNA sequence alignment in order to predict a consensus RNA secondary structure. The input to the network is the mutual information, the fraction of complementary nucleotides, and a novel consensus RNAfold secondary structure prediction of a pair of alignment columns and its nearest neighbors. Given this input, the network computes a prediction as to whether a particular pair of alignment columns corresponds to a base pair. By using a comprehensive test set of 49 RFAM alignments, the program KNetFold achieves an average Matthews correlation coefficient of 0.81. This is a significant improvement compared with the secondary structure prediction methods PFOLD and RNAalifold. By using the example of archaeal RNase P, we show that the program can also predict pseudoknot interactions

Protein Structure Optimization using a Combinatorial Search Algorithm

We developed a combinatorial search algorithm which we call best profile search for the global optimization of protein structures. This algorithm finds near optimal solutions in an early stage of the optimization. We developed a protein structure scoring function which depends on the distances and orientations of amino acid pairs. We devised an iterative procedure in order to improve the predictive power of the scoring function: We use this best profile search optimization procedure for the ab initio computation of low energy structures of a test set of proteins using our scoring function. This set of ab initio structure calculation is embedded into a second level of optimization: The mean RMSD results of the structure optimizations are taken as an objective function for a simulated annealing procedure. The simulated annealing algorithm minimizes the geometric mean RMSD deviations of the computed structures with respect to their native structures. RESULTS: With the developed scoring function we obtain for proteins of the test set for ab initio structure optimizations a RMSD deviation of 5.2 Angstroem in the geometric mean, taking the best structure among the top ten scoring structures found during structure optimization